Email this article Predicting On-time Graduation based on Student Performance in Core Introductory Computing Courses using Decision Tree Algorithm
Abstract
Abstract: Objectives: This study primarily aimed at developing a model that will predict whether a student will graduate on time based on their academic performance in their respective core introductory computing courses. Methods: The educational data mining process was employed in the conduct of this research. The process commenced with the collection of educational data and culminated with the evaluation of the developed model. This research utilized the decision tree algorithm. Findings: The model evaluation resulted to an 88.9 percent classification accuracy where the total number of actual Yes (students who graduated on-time) is 52,49 were classified correctly and 3 were misclassified as No in the prediction and the total number of actual No (students who did not graduated on-time) is 20,15 of which were classified correctly and 5 were misclassified in the prediction. Conclusion: Results of the study can be used as inputs in the crafting of new resource materials and an improved curriculum that will help improve the performance of students in the database management course. The model can also be used as a tool to help students graduate on-time.
Keywords: decision tree, prediction, on-time graduation.
Abstrak: Tujuan: Studi ini ditujukan untuk mengembangkan model yang akan memprediksi apakah seorang siswa akan lulus tepat waktu berdasarkan performa akademik mereka dalam mata kuliah pengantar komputasi. Metode: Proses data mining pendidikan digunakan dalam penelitian ini. Prosesnya dimulai dengan pengumpulan data pendidikan dan diakhiri dengan evaluasi model yang dikembangkan. Penelitian ini menggunakan decision tree algorithm. Temuan: Evaluasi model menghasilkan akurasi pengklasifikasian hingga 88,9 persen di mana jumlah total jawaban Ya (siswa yang lulus tepat waktu) adalah 52,49 yang diklasifikasikan dengan benar dan 3 salah diklasifikasikan sebagai Tidak dalam prediksi dan jumlah total jawaban Tidak (siswa yang tidak lulus tepat waktu) adalah 20,15 di antaranya diklasifikasikan dengan benar dan 5 salah diklasifikasikan dalam prediksi. Kesimpulan: Hasil penelitian dapat digunakan sebagai masukan dalam penyusunan bahan ajar baru dan perbaikan kurikulum yang akan membantu meningkatkan kinerja mahasiswa pada mata kuliah manajemen basis data. Model juga dapat digunakan sebagai alat untuk membantu mahasiswa lulus tepat waktu.
Kata kunci: decision tree, prediksi, lulus tepat waktu.
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